Prioritization of potential drug targets for diabetic kidney disease using integrative omics data mining and causal inference.

ABSTRACT: Diabetic kidney disease (DKD) with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression. Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials. In this study, we integrated large-scale plasma proteomics, genetic-driven causal inference, and experimental validation to identify prioritized targets for DKD using the UK Biobank (UKB) and FinnGen cohorts. Among 2844 diabetic patients (528 with DKD), we identified 37 targets significantly associated with incident DKD, supported by both observational and causal evidence. Of these, 22% (8/37) of the potential targets are currently under investigation for DKD or other diseases. Our prospective study confirmed that higher levels of three priori

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12446642 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature